One main advantage of monocular depth estimation is its potential in dramatically reducing the hardware cost for 3D depth acquisition. Motivated by this, many studies have been proposed to solve this problem.
The basic idea of supervised monocular estimation methods is to train a network that directly learns the mapping from the input RGB pixels to the real-value output depth in a single stage. For example, Laina et al. [1]} proposed FCRN which uses ResNet-50 [2]} as backbone, followed by multiple up-projection modules. Hu et al. [3]} leveraged SENet-154 [4]} as encoder together with multi-scale fusion module.
